Speech recognition and summarization

ABSTRACT

The subject matter of this specification can be embodied in, among other things, a method that includes receiving two or more data sets each representing speech of a corresponding individual attending an internet-based social networking video conference session, decoding the received data sets to produce corresponding text for each individual attending the internet-based social networking video conference, and detecting characteristics of the session from a coalesced transcript produced from the decoded text of the attending individuals for providing context to the internet-based social networking video conference session.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is a continuation of, and claims priority under35 U.S.C. § 120 from, U.S. patent application Ser. 15/202,039, filed onJul. 5, 2016, which is a continuation of U.S. patent application Ser.No. 14/078,800, filed on Nov. 13, 2013, which is a continuation of U.S.patent application Ser. No. 13/743,838, filed on Jan. 17, 2013, whichclaims priority under 35 U.S.C. § 119(e) from, U.S. ProvisionalApplication 61/699,072, filed on Sep. 10, 2012. The disclosures of theseprior applications are considered part of the disclosure of thisapplication and are hereby incorporated by reference in theirentireties.

TECHNICAL FIELD

This specification generally relates to speech recognition.

BACKGROUND

In certain automated speech recognition (ASR) implementations, a userfinishes speaking before recognition results are displayed or actedupon.

SUMMARY

In speech recognition and summarization, partial results can be streamedout from a recognizer while the user is speaking, thereby enabling, forexample, a number of useful features for spoken language interfaces. Forexample, the recognizer can act on or show the user one or morecontextual suggestions, such as additional information related to thetopic of the user's speech (e.g., partial transcriptions, hyperlinks,maps). Along with these and other useful features, contextualsuggestions can be offered substantially in real-time to augment orenhance the user's speech.

According to one general implementation of the subject matter describedby this specification, a system can receive conversational speech fromtwo or more users. The speech data can be processed to identify a topicor key words/phrases, for example, by detecting repeated words ortopical words used by multiple users in the conversation, by detectingtonal characteristics of the speech (e.g., stressed words), or bydetecting other characteristics. Additionally, other data (e.g.,videoconference images) can also be received and processed to identifytopic or key words/phrases, for example, by identifying users' bodylanguage or facial expressions as an indicator of the importance ofrecently spoken speech audio (e.g., a user who looks confused whileanother user recites a phone number may benefit from seeing atranscription of the phone number).

Described herein are techniques for speech recognition and summarizationthat include receiving two or more data sets each representing speech ofa corresponding individual attending an internet-based social networkingvideo conference session, decoding the received data sets to producecorresponding text for each individual attending the internet-basedsocial networking video conference, and detecting characteristics of thesession from a coalesced transcript produced from the decoded text ofthe attending individuals for providing context to the internet-basedsocial networking video conference session.

Implementations of the techniques can include some, all, or none of thefollowing features. The technique can also include detectingcharacteristics of the session from the two or more received data sets.The technique can also include detecting characteristics of the sessionattending individuals from other corresponding data sets. Detectingcharacteristics of the session can include monitoring at least one ofthe volume of the speech represented in the two or more data sets andthe presented speed of the speech represented in the two or more datasets. Detecting characteristics of the session attending individuals caninclude detecting physical features of the attending individuals.Detecting characteristics of the session from the coalesced transcriptcan include at least one of detecting the temporal length of the sessionand detecting repetitive use of one or more words. Detectingcharacteristics of the session from the coalesced transcript can includedetecting a topic from the content of the transcript. Detectingcharacteristics of the session from the two or more received data setscan include detecting an emotion of one or more of the attendingindividuals. The physical feature of the attending individuals caninclude facial expressions. Detecting repetitive use of one or morewords can include associating a statistical weighting value with each ofthe one or more words based upon at least one of the number of detectedrepetitive uses of each of the one or more words and temporal length oftime between the detected repetitive uses. Detecting characteristics ofthe session from the coalesced transcript can include associatingstatistical weighting values with each topic detected from the contentof the transcript. Detecting characteristics of the session from thecoalesced transcript can include associating statistical weightingvalues to one or more words associated with each topic detected from thecontent of the transcript. The statistical weighting value can be basedat least partly on the number of the attending individuals who used theone or more words. The techniques can further comprise providing thecontext to one or more of the attending individuals, detecting aninteraction by one or more of the attending users with the context, anddetecting further characteristics of the session from the detectedinteraction.

The systems and techniques described herein, or portions thereof, may beimplemented as a computer program product that includes instructionsthat are stored on one or more non-transitory machine-readable storagemedia, and that are executable on one or more processing devices. Thesystems and techniques described herein, or portions thereof, may beimplemented as an apparatus, method, or electronic system that mayinclude one or more processing devices and memory to store executableinstructions to implement the stated functions.

The systems and techniques described here may provide one or more of thefollowing advantages. First, a system can identify topics or importantselections of information spoken during a conversation. The system canuse audible or visual cues provided by one or more participants toidentify words or phrases that may be of use to the participants. Thesystem can reformat or supplement the identified topics or selections ofspoken information using information obtained by using the topics orselections of spoken information as search queries. The system canaugment a conversation between two or more participants by providingtranscriptions of identified topics or selections of speech, or byproviding information based on the identified topics or selections ofspeech.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features andadvantages will be apparent from the description and drawings, and fromthe claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram of an example system that can recognize andsummarize speech data.

FIG. 2 is a block diagram showing an example network environment onwhich the process described herein for recognizing, summarizing, andusing recognized speech data may be implemented.

FIG. 3 is a diagram of another example system that can recognize,summarize, and use speech data.

FIG. 4 is a flow diagram of an example process recognizing, summarizing,and using recognized speech data.

FIG. 5 is a flow diagram of an example process for recognizing,summarizing, and using recognized speech data.

FIG. 6 is a block diagram of computing devices that may be used toimplement the systems and methods described in this document.

DETAILED DESCRIPTION

This document describes systems and techniques for recognizing andsummarizing speech data. In general, two or more users can participatein an audio or video conference (e.g., a voice-enabled instant messengerapplication, a two-way or multi-way videoconference, a telephone call)while a speech recognition system processes their speech. The speechrecognition system processes speech and other data to identify topics,subjects, key words, and key phrases from the conversation, and usesthat information to augment the conversation by providing the users withinformation that they may find useful. In an example that many peoplehave probably experienced, one person in teleconference orvideoconference may quickly blurt out a phone number or address fasterthan the listeners can react to capture it. In the example systems, thespeech recognition system may identify what the speaker said, identifythe utterance for transcription, and provide the other participants witha written transcription of the phone number or address.

FIG. 1 is a diagram of an example system 100 that can recognize,summarize, and use recognized speech data. For example, the examplesystem 100 can recognize and summarize speech data from audio and videodata 112 made by a user 102 and captured by a user device 106 (e.g., acomputer system) though a microphone 107 and a video camera 108 as theuser 102 participates in an Internet-based social networking videoconference session 180. The system 100 can identify segments that may beof interest to participants 182 in the video conference session 180, andoutput a collection of segments 184 to the participants 182 through acontext interface 186.

Although not illustrated in FIG. 1, each of the participants 182participates in the video conference session 180 using respective userdevices 106. Each of the participants 182 sees the video conferencesession 180 and the collection of segments 184 presented through arespective context interface 186.

In further detail, the user device 106 is in communication with anautomated speech recognition (ASR) engine 109. The user device 106 maybe any appropriate type of computing device, including but not limitedto a mobile phone, smart phone, PDA, music player, e-book reader, tabletcomputer, laptop or desktop computer, or other stationary or portabledevice, that includes one or more processors and computer readablemedia. The ASR engine 109 may be a component of the mobile device 106.In some implementations, the ASR engine 109 may be external to the userdevice 106, and communication between the user device 106 and the ASRengine 109 may take place over phone and/or computer networks includinga wireless cellular network, a wireless local area network (WLAN) orWi-Fi network, a Third Generation (3G) or Fourth Generation (4G) mobiletelecommunications network, or any appropriate combination thereof.

Audio and video data 112 is sent to the ASR engine 109. For example,when the user 102 begins to utter a sentence (e.g. “. . . when we get toDuluth, let's stay at the Superior Hotel. Call me at 555-123-4567 whenyou arrive . . . ”), the utterance 113 is encoded and communicated tothe ASR engine 109 as part of the audio and video data 112.Additionally, the audio and video data 112 may include images orstreaming video captured using the camera 108. For example, the audioand video data 112 may include a video stream of the user's face 114.

The ASR engine 109 receives and processes the audio and video data 112.The ASR engine 109 may be configured to execute application codeassociated with a variety of software components (e.g., modules,objects, libraries, services, and the like) for implementing a speechsummarizer 115, including a recognizer 116, a detector 118, and anoutput module 122. The ASR engine 109 is also in communication with anInternet-based social networking video conference service 130.

As the ASR engine 109 receives the audio and video data 112, therecognizer 116 recognizes and converts the utterance 113 into text. Thetext is also processed by the detector 118. In the example system 100,the detector 118 identifies topics and/or key words recognized by therecognizer 116. As described in greater detail below, the recognizer 116can identify potentially “important” words in the utterance 113.

The output module 122 forms a collection of context data 124 from the“important” information provided by the detector 118, and provides thecontext data 124 to the Internet-based networking video conferenceservice 130 which communicates them to the user device 106 through thecontext interface 186 as the collection of segments 184. The contextdata 124 may be sent to the user device 106 at pre-determined timeintervals, or in real-time as segments are identified by the summarizer115 of the ASR engine 109. In some implementations, the context data 124and the collection of segments 184 can include text, hyperlinks,numbers, graphics, or user interface components. For example, the user102 may speak the phone number “555-123-4567”, and the string“555-123-4567” may appear among the collection of segments 184, possiblyas a hyperlink that when selected will initiate a phone call using thedisplayed number. In another example, the user 102 may make an utterancethat can be identified as a street address, and the address may appearamong the collection of segments 184 as a map or text, possiblyaccompanied by user interface elements that can be selected to open anenlarged map view or to obtain navigational directions. In yet anotherexample, words or phrases from the utterance 113 may be selected andused to provide knowledge graphs (e.g., a summary of information about atopic and/or links to additional information) among the collection ofsegments 184.

The video conference session 180 displays the collection of segments 184that are received by the user device 106. In the example shown, thevideo conference session 180 incrementally displays the segments 184 asthey arrive. For the example, the context interface 186 may be ascrollable window in which each of the segments 184 appears as they areidentified and output to the user device 106. In such an example, thecollection of segments 184 can appear as a time-ordered list of segmentsthat the user can scroll through and interact with at a later time.

The process described above may be implemented in an appropriate networkenvironment, with appropriate devices and computing equipment. Anexample of such an environment is described below.

FIG. 2 is a block diagram showing an example network environment onwhich the processes described herein for suggesting interaction amongmembers of a social network may be implemented. In this regard, FIG. 2shows an example network environment 200. Network environment 200includes computing devices 202, 204, 206, 208, 210 that can eachcommunicate with a first server system 212 and/or a second server system214 over a network 211. Each of computing devices 202, 204, 206, 208,210 has a respective user 222, 224, 226, 228, 230 associated therewith.The first server system 212 includes a computing device 216 and amachine-readable repository, or database 218. The second server system214 includes a computing device 220 and a machine-readable repository,or database 222. Example environment 200 may include many thousands ofWeb sites, computing devices and servers, which are not shown.

The network 211 can include a large computer network, e.g., a local areanetwork (LAN), wide area network (WAN), the Internet, a cellularnetwork, or a combination thereof connecting a number of mobilecomputing devices, fixed computing devices, and server systems. Thenetwork(s) may provide for communications under various modes orprotocols, e.g., Transmission Control Protocol/Internet Protocol(TCP/IP), Global System for Mobile communication (GSM) voice calls,Short Message Service (SMS), Enhanced Messaging Service (EMS), orMultimedia Messaging Service (MMS) messaging, Code Division MultipleAccess (CDMA), Time Division Multiple Access (TDMA), Personal DigitalCellular (PDC), Wideband Code Division Multiple Access (WCDMA),CDMA2000, or General Packet Radio System (GPRS), among others.Communication may occur through a radio-frequency transceiver. Inaddition, short-range communication may occur, e.g., using a Bluetooth,WiFi, or other such transceiver.

Computing devices 202 to 210 enable respective users 222 to 230 toaccess Internet-based social networking video conference services, e.g.,the Internet-based social networking video conference service 130. Insome examples, users 222 to 230 can be members of a social networkingservice. For example, user 222 of computing device 202 can view a Webpage using a Web browser. The Web page can be provided to computingdevice(s) 202 to 210 by server system 212, server system 214 or anotherserver system (not shown). The Web page may be internal to the socialnetworking service or the Web page may be a publicly accessible Web pagethat is not part of the social networking service, and can includefeatures of the Internet-based social networking video conferenceservice 130.

In example environment 200, computing devices 202, 204, 206 areillustrated as desktop-type computing devices, computing device 208 isillustrated as a laptop-type computing device 208, and computing device210 is illustrated as a mobile computing device. In someimplementations, any of the computing devices 202-210 can be the userdevice 106. It is appreciated, however, that computing devices 202 to210 can each include a type of computing device, examples of whichinclude a desktop computer, a laptop computer, a handheld computer, apersonal digital assistant (PDA), a cellular telephone, a networkappliance, a camera, a smart phone, an enhanced general packet radioservice (EGPRS) mobile phone, a media player, a navigation device, anemail device, a game console, or a combination of two or more of thesedata processing devices or other appropriate data processing devices. Insome implementations, a computing device can be included as part of amotor vehicle (e.g., an automobile, an emergency vehicle (e.g., firetruck, ambulance), a bus).

FIG. 3 is a diagram of another example system 300 that can recognize,summarize, and use recognized speech data. Along with providing thefunctionality of the system 100 (presented in FIG. 1), the system 300 isadapted to provide contextual information for voice (e.g., telephone)communications between two or more users, such as a user 302 a and auser 302 b. For example, the example system 300 can recognize andsummarize speech data from audio data 312 made by a user 302 a andcaptured by a user device 306 as the user 302 a participates intelephone call or teleconference. The system 300 can identify segmentsthat may be of interest to the users 302 a and 302 b during the phonecall, and output a collection of segments 384 to the users 302 a, 302 bthrough a context interface 386.

In further detail, the user devices 306 a and 306 b are in communicationwith an automated speech recognition (ASR) engine 309. The user devices306 a, 306 b may be any appropriate type of computing device, includingbut not limited to a mobile phone, smart phone, PDA, music player,e-book reader, tablet computer, laptop or desktop computer (e.g.,running a voice-over-IP or other form of audio communicationsapplication), or other stationary or portable device, that includes oneor more processors and computer readable media. The ASR engine 309 maybe a component of the mobile devices 306 a and/or 306 b. In someimplementations, the ASR engine 309 may be external to the user devices306 a and/or 306 b, and communication between the user devices 306 a,306 b and the ASR engine 309 may take place over phone and/or computernetworks including a wireless cellular network, a wireless local areanetwork (WLAN) or Wi-Fi network, a Third Generation (3G) or FourthGeneration (4G) mobile telecommunications network, or any appropriatecombination thereof.

Audio data 312 is sent to the ASR engine 309. For example, when the user302 a begins to utter a sentence (e.g. “. . . when we get to Duluth,let's stay at the Superior Hotel. Call me at 555-123-4567 when youarrive . . . ”), the utterance 313 is encoded and communicated to theASR engine 309 as part of the audio data 312.

The ASR engine 309 receives and processes the audio data 312. The ASRengine 309 may be configured to execute application code associated witha variety of software components (e.g., modules, objects, libraries,services, and the like) for implementing a speech summarizer 315,including a recognizer 316, a detector 318, and an output module 322.

As the ASR engine 309 receives the audio data 312, the recognizer 316recognizes and converts the utterance 313 into text. The text is alsoprocessed by the detector 318. In the example system 300, the detector318 identifies topics and/or key words recognized by the recognizer 316.As described in greater detail below, the recognizer 316 can identifypotentially “important” words in the utterance 313.

The output module 322 forms a collection of context data 324 from the“important” information provided by the detector 318, and provides thecontext data 324 to the user devices 306 a and 306 b through the contextinterface 386 as the collection of segments 384. The context data 324may be sent to the user devices 306 a, 306 b at pre-determined timeintervals, or in real-time as segments are identified by the summarizer315 of the ASR engine 309. In some implementations, the context data 324and the collection of segments 384 can include text, hyperlinks,numbers, graphics, or user interface components. For example, the user302 a may speak the phone number “555-123-4567”, and the string“555-123-4567” may appear among the collection of segments 384, possiblyas a hyperlink that when selected will initiate a phone call using thedisplayed number. In another example, the user 302 a may make anutterance that can be identified as a street address, and the addressmay appear among the collection of segments 384 as a map or text,possibly accompanied by user interface elements that can be selected toopen an enlarged map view or to obtain navigational directions. In yetanother example, words or phrases from the utterance 313 may be selectedand used to provide knowledge graphs (e.g., a summary of informationabout a topic and/or links to additional information) among thecollection of segments 384.

Context interfaces 386 display the collection of segments 384. In theexample shown, the context interfaces 386 incrementally display thesegments 384 as they arrive. For the example, the context interfaces 386may be scrollable windows in which each of the segments 384 appears asthey are identified and output to the user device 306 a, 306 b. In suchan example, the collection of segments 384 can appear as a time-orderedlist of segments that the user can scroll through and interact with at alater time.

FIG. 4 is a block diagram of an example process 400 for recognizing,summarizing, and using the recognized speech data. In someimplementations, the process 400 can be used by an ASR such as the ASR109 and/or ASR 309.

In the illustrated example, a transcript builder 402 receives acollection of speech data 404 a, 404 b, and 404 n from a collection ofusers. For example, the collections of speech data 404 a-404 n can bedigitized samples of user speech (e.g., the utterance 113) each spokenby a different user, and captured and converted by the user devices 106,306 a, 306 b or the ASRs 109, 309. The transcript builder 402 processesthe speech data 404 a-404 n to identify words and phrases and convertthe words and phrases to text.

The transcript builder 402 also provides annotation data along with thetranscribed text to form an annotated transcript 406. The annotatedtranscript 406 includes data that describes attributes of thetranscribed text. For example, the annotated transcript 406 can indicatethat a particular phrase had been used or repeated by multiple users inthe conversation (e.g., thereby inferring that the phrase may have beenof importance in the conversation). In another example, the annotatedtranscript 406 can indicate that a particular phrase had been spokenwith emphasis (e.g., spoken loudly, spoken at a different rate, spokenwith a different pitch or tone, spoken with different enunciation). Inanother example, the annotated transcript 406 can indicate that aparticular phrase had been spoken in the context of other words ofphrases that may indicate importance (e.g., “this is important”, “writethis down . . . ”, “don't forget . . . ”, “make sure . . . ”).

The transcript builder 402 receives a collection of video data 408 a-408n. Each collection of video data 408 a-408 n corresponds to a respectivecollection of the speech data 404 a-404 n. For example, the speech data404 a and the video data 408 a may be the audio and video parts of auser's video conference feed in an Internet-based social networkingvideo conference session.

The transcript builder 402 processes the video data 408 a-408 n alongwith the speech data 404 a-404 n to determine additional annotationinformation that can be included in the annotated transcript 406. Forexample, the physical movements or facial expressions of a user who iscurrently speaking can indicate that the speaker is putting particularemphasis upon a particular segment of he is saying (e.g., and thereforethe segment may be a good candidate for presentation as contextualinformation). In another example, the physical movements or facialexpressions of a user who is currently listening to a segment of speechcan indicate that the listener is particularly interested in or confusedby what is being said (e.g., and therefore one or more of the listenersmay benefit from having the segment transcribed and provided as contextdata).

A context builder 410 uses the annotated transcript 406 to buildsegments of contextual information. In some implementations, the contextbuilder 410 can identify segments of transcribed speech data and providethem as a collection of context data 412. For example, the contextbuilder 410 may identify a segment of the annotated transcript as anemail address and provide the transcribed email address as context data;furthermore, the context builder 410 may reformat the email address as ahyperlink (e.g., mailto:user@example.com) and provide the link ascontext data. In some implementations, the context data 412 can be thecontext data 124 or 324.

In some implementations, the context builder 410 can use annotations inthe annotated transcript 406 to identify information that can beprovided at context data. For example, a speaker may say, “whateverhappens, we all need to be finished by NINE O'CLOCK in the morning,”(e.g., saying the time with emphasis that is noted in the annotations).The context builder 410 may identify that the words “nine o'clock” werestressed, and provide “9:00 am” as context data.

The context builder 410 also receives advertising data from anadvertising module 414. The advertising module 414 identifies textual,graphical, and/or multimedia advertising content based on the contextdata 412, and provides the advertising content to the context builder410. For example, the annotated transcript 406 may include a discussionabout an upcoming family vacation to “Walley World”. As a result, theterm “Walley World” may appear among the context data 412. Theadvertising module 414 may use this term to identify an advertisementfor “Walley World” and provide the ad to the context builder 410, whichcan in turn incorporate the advertisement into the context data 412.

The context builder 410 also receives search data from an ambient searchmodule 416. The ambient search module 416 performs Internet and/or otherinformational search queries based at least in part upon the contextdata 412, and provides the results as the search data to the contextbuilder 410. In some implementations, the ambient search module 416 canprovide knowledge graphs or other summaries of information obtained ascontext data. For example, the context data 412 may include the phrase“Veterans Day”, and the ambient search module 416 may perform searchesto identify information about Veterans Day, such as the history of theholiday and the date of its next occurrence (e.g., in the U.S., VeteransDay is observed on the weekday closest to November 11 each year).

In some implementations, the context builder 410 can perform otherfunctions to create the context data 412. For example, a phrase may beidentified as a physical address or landmark, and the context builder410 can provide a hyperlink to a map, a map image, navigationdirections, information about the address (e.g., current time, weatherconditions), or combinations of these and other appropriate informationthat can describe an address or landmark. In another example, a phrasemay be identified as a telephone number, and the context builder 410 canprovide the phone number as a hyperlink or other user interface elementthat can be used to initiate a telephone call to the number.

In another example, the phrase “let's plan to meet at my house nextFriday at 8 pm,” can be processed by the context builder to create ameeting or event invitation. The sub-phrase “next Friday at 8 pm” can beprocessed to identify a calendar date and time to be associated with theevent. The sub-phrase “at my house” can be processed to identify alocation based on information known about the user who spoke the phrase(e.g., the speaker's home address may be drawn from public profileinformation). The sub-phrase “let's” may be processed to infer a guestlist for the event (e.g., “let's” may imply that all the participants inthe conversation can be included as guests). Participants can click theinvitation to populate their calendar with the event, which may includethe date, time, location, and/or guest list.

The context data 412 includes a collection of speaker contexts 418 a-418n. Each of the speaker contexts 418 a-418 n corresponds to one of thecollections of speech data 404 a-404 n (e.g., each participant in theconversation has a speaker context associated with them). For example,two participants in a conversation may be debating over where to take avacation; one participant may suggest a trip to Sweden while the othermay suggest a trip to Japan. While the overall context data 412 of theconversation may be identified by the context builder 410 as pertainingto “travel”, the speaker context 418 a of one participant may pertain to“Sweden travel” while the speaker context 418 n of the other participantmay pertain to “Japan travel”.

In some implementations, the context data 412 may be tailored to eachparticipant in a conversation. For example, a speaker may say to alistener, “my phone number is 555-123-4567”. The context builder 410 mayidentify the speaker and the listener and provide each with differentcontext data, such as by providing a transcript of the phone number tothe listener but not to the speaker (e.g., the speaker already knows thephone number and therefore may not benefit from seeing a transcription).

The context data 412 is provided to the transcript builder 402. In someimplementations, the transcript builder 402 can use the context data 412to improve the transcription of the speech data 404 a-404 n. Forexample, at the start of a conversation the topic or context of theconversation may be unknown to the transcript builder 402, and thetranscript builder 402 may transcribe the speech data 404 a-404 n usinga general purpose transcription engine. As the conversation progresses,the context builder 410 may identify context data 412 that can helpidentify the context of topic of the conversation. Based on the contextdata 412, the transcript builder 402 may be able to select one or morespecial purpose (e.g., topic specific) transcription engines that may beused to identify and transcribe words that may not commonly be used ineveryday speech.

For example, a group of doctors may use an Internet-based socialnetworking video conference session to identify the treatment thatshould be used for a particular patient. At the start of theconversation, the transcript builder 402 may use a general purposetranscription engine to identify words such as “doctor”, “St. OlafHospital”, “treatment”, “medication”, and “cancer”, but such a generalpurpose transcription engine may not be able to identify medicalterminology used to describe things such as anatomy, pathogens, orpharmaceuticals. The context builder 410 can use this information togenerate the context data 412, which may initially include generalcontext information such as a segments that provides a map to “St. OlafHospital”. The transcript builder 402 can use the context data 412 todetermine that the conversation is about medicine, and respond byengaging a medical transcription engine that is able to identify andtranscribe medical terminology. For example, the transcript builder 402may now be able to transcribe the term “methotrexate” (e.g., a cancerdrug) which the context builder 410 can use to engage the ambient searchmodule 416 to obtain related information such as brand names, medicaldictionary definitions, known side effects, or other appropriateinformation that may be useful in the form of context data 412 that maybe provided to the participants in the conference.

The context data 412 is also provided to a conversation discovery andsearch module 450. The discovery and search module 450 identifies andsuggests conversations (e.g., other Internet-based social networkingvideo conference sessions) that may be contextually similar to the onebeing processed by the context builder 410. For example, the discoveryand search module 450 can help users find other conversations that theymight be interested to join.

The discovery and search module 450 receives a collection ofconversation context data 452 a-452 n. The conversation context data 452a-452 n are collections of context data, such as the context data 412,generated from other conversations taking place among other users. Thediscovery and search module 450 compares the context data 412 to theconversation context data 452 a-452 n to identify other (e.g.,contextually similar) conversations that may be of interest to users.The identified conversations are provided as one or more conversationsuggestions 454. In some implementations, the conversation suggestions454 can be provided to users to allow them to join one of the suggestedconversations. For example, the conversation suggestions 454 can beprovided as one or more hyperlinks that can be selected to join the userto the selected conversation.

The conversation suggestions 454 are provided back to the discovery andsearch module 450 to further refine the process of identifyingadditional suggestions. For example, by selecting one of theconversation suggestions 454, the user may trigger feedback that canindicate to the discovery and search module 450 that the suggestion wasa good one.

FIG. 5 is a flow chart of another example process 500 for therecognition, summarization, and use of recognized speech data. In someimplementations, the process 400 can be used by the ASR 109 and/or ASR309. In the example of the process 500, a collection of speech data sets502 a-502 n are provided, for example, by capturing and digitizing thespeech utterances of one or more users (e.g., the user 102) by userdevices (e.g., the user device 106).

Two or more data sets (e.g., the speech data sets 502 a-502 n), eachrepresenting speech of a corresponding individual attending aninternet-based social networking video conference session are received(510). For example, the ASR 109 can receive the audio and video data 112from the user device 106, in which the audio and video data 112 includesa representation of the utterance 113 made by the user 102.

The received data sets are decoded to produce corresponding text foreach individual attending the internet-based social networking videoconference (520). For example, the recognizer 116 can transcribe theaudio and video data 112.

Characteristics of the session are detected from a coalesced transcriptproduced from the decoded text of the attending individuals forproviding context to the internet-based social networking videoconference session (530).

In some implementations, characteristics of the session can be detectedfrom the two or more received data sets. In some implementations,detecting characteristics of the session can include monitoring at leastone of the volume of the speech represented in the two or more data setsand the presented speed of the speech represented in the two or moredata sets. For example, the sets of user speech data 502 a-502 n caneach be audio data representing the speech of respective users. Thespeech data 502 a-502 n can be processed individually or in combinationto identify characteristics of each user's speech and/or characteristicsof the conversation as a whole. For example, an individual user's speechmay place additional levels of stress or emphasis on some words than maybe placed on others (e.g., the user may say some things detectablylouder or in a different tone), and these levels may be identified ascharacteristics of the session. In another example, the speech patternsof two or more conversation participants may alter in response toemphasized or important utterances (e.g., the users may speak relativelyloudly or rapidly, or may overlap each other during a heated debate),and these patterns may be identified as characteristics of theconversation.

In some implementations, characteristics of the session attendingindividuals can be detected from other corresponding data sets, such asa data set 532. For example, the data set 532 can include video data,such as a video data portion of the audio and video data 112 captured bythe camera 108. In some implementations, detecting characteristics ofthe session attending individuals can include detecting physicalfeatures of the attending individuals. A physical features detectionprocess 539 may be used to analyze the physical features of a user ascaptured by video data. In some implementations, the physical featurecan be the attending individual's facial expressions. For example, ifthe user 102 is detected to be unusually wide-eyed, the detector module118 may determine that the user is emphasizing something currently beingsaid. In another example, the user 102 may furl his brow while listeningto another user speak (e.g., an address, a complicated series ofnumbers, a name with a difficult spelling), and the detector module 118may determine that the user is concerned or confused by what is beingsaid. In these and other examples, the summarizer 115 may use thedetected characteristics to identify and provide segments as contextdata 124 that may be useful to the attending individuals. For example,the confused user of the previous example may benefit from seeing atranscript of the segment of speech that was spoken at approximately thetime when the user furled his brow.

In some implementations, detecting characteristics of the session fromthe coalesced transcript can include at least one of detecting thetemporal length of the session and detecting repetitive use of one ormore words. A repeated words detection process 534 can be used to detectwhen various words or phrases have been used multiple times during thecourse of a conversation. For example, a user may speak a particularword or phrase several times during a conversation, and the repeatedword or phrase may be annotated to indicate the possible contextualimportance of the repeated utterance (e.g., the phrase was repeatedbecause it may be the topic of the conversation, or may have beenrepeated to emphasize its importance).

In another example, two or more participants in a conversation maycommonly use a particular word or phrase during a conversation, and thecommonly used word or phrase may be identified to indicate its possiblecontextual importance. For example, when the word or phrase is used bymultiple attending individuals, it may be inferred that the word orphrase may be of importance in the conversation and therefore may beannotated to reflect its possible importance in the context of theconversation.

A temporal length detection process 536 can be used to detect theduration of various utterances during the course of a conversation. Forexample, when a user is detected as speaking substantially interruptedfor extended periods (e.g., the speaker is giving a presentation orlecture), that user's speech may be annotated to reflect possibleimportance to the other users.

In some implementations, detecting characteristics of the session fromthe coalesced transcript can include detecting the volume of one or moreusers' speech. A volume detection process 537 can be used to detectvolume characteristics associated with various utterances. For example,if a user speaks a particular phrase more loudly than others, thedetector 118 may annotate the respective transcription of the phrase toreflect the emphasis with which it was spoken. Since the phrase wasdetected as being said with emphasis, the phrase may be selected to beprovided as context data for the individuals attending the conversation.

In some implementations, detecting characteristics of the session fromthe two or more received data sets can include detecting an emotion ofone or more of the attending individuals. For example, a user's choiceof words, patterns of speech (e.g., speed, volume, pitch, pronunciation,voice stress), gestures, facial expressions, physical characteristics,body language, and other appropriate characteristics can be detected byan emotion detection process 538 to estimate the user's emotional state.In some implementations, a user's emotional state can be used toidentify information that may be useful to the user as context data. Forexample, a listener may look or act excited upon hearing informationfrom a speaker (e.g., “I am buying you a new ‘SuperDuper X’ phone”), andthe summarizer 115 may use the listener's emotional reaction as a cue tosupply the listener with context data relating to what the speaker said(e.g., product information for the ‘SuperDuper X’ phone).

In some implementations, detecting characteristics of the session fromthe coalesced transcript can include detecting a topic from the contentof the transcript. For example, during the course of a conversation,many words and phrases may be used, with many of them not necessarilybeing important to the core subject of the discussion. The detector 118may analyze the words and phrases and identify one or more topics towhich they may pertain. Once the likely topic(s) of the conversationhave been identified, the detector 118 may detect additional topicalwords in the transcription and annotate them as being potentiallyrelevant to the topic of the conversation. In such examples, words andphrases that pertain to the topic of the conversation can be usedidentify context data that may be useful to the attending individualsduring the course of their discussion.

In some implementations, detecting characteristics of the session fromthe coalesced transcript can include detecting repetitive use of one ormore words and associating a statistical weighting value with each ofthe one or more words based upon at least one of the number of detectedrepetitive uses of each of the one or more words and temporal length oftime between the detected repetitive uses. For example, when a word isspoken and identified repeatedly during a conversation, additionalstatistical weight may be associated with the word (e.g., when a userspeaks a word repeatedly, the word may have special importance in thecontext of the conversation). In some implementations, the statisticalweighting value can be based at least partly on the number of theattending individuals who used the one or more words. For example, whenmultiple participants in a conversation are detected as having spoken acommon word or phrase, that word or phrase may be given additionalstatistical weight (e.g., when more than one user speaks a common word,the word may have special importance in the context of theconversation).

In some implementations, detecting characteristics of the session fromthe coalesced transcript can include associating statistical weightingvalues with each topic detected from the content of the transcript. Forexample, the words “shutter”, “f-stop”, “aperture” and “focus” can berecognized as a photography domain. In some examples, assigningadditional statistical weight to topic domains associated with detectedcharacteristics can be used is to increase speech recognition accuracybased on identification of several words within the same domain. Forexample, speech recognition can include assigning probabilities tovarious candidate words that are identified as being possibletranscriptions for a spoken word. In one example, the word “f-stop” inisolation may have a low probability since it's not a commonly usedphrase in everyday conversation, and a phrase like “bus stop” maygenerally be a more likely candidate. However, in a conversationcontaining other words such as “aperture” and “focus”, the phrase“f-stop” may be given a higher probability as a candidate transcription.

In another example, the words “shutter”, “f-stop”, “aperture” and“focus” can be recognized as a photography domain. In response, anincreased statistical weighting value can be associated with the topicof “photography”. In such examples, the statistical weighting valueassociated with a topic can be used to identify relevant informationthat can be provided to participants in the conversation. For example,by identifying the topic of “photography”, and detecting the spoken useof the word “lens”, information about camera lenses rather than eyeglasslenses may be identified and provided to the attending individuals.

FIG. 6 is a block diagram of computing devices 600, 650 that may be usedto implement the systems and methods described in this document, eitheras a client or as a server or plurality of servers. Computing device 600is intended to represent various forms of digital computers, such aslaptops, desktops, workstations, personal digital assistants, servers,blade servers, mainframes, and other appropriate computers. Computingdevice 650 is intended to represent various forms of mobile devices,such as personal digital assistants, cellular telephones, smartphones,and other similar computing devices. The components shown here, theirconnections and relationships, and their functions, are meant to beexemplary only, and are not meant to limit implementations of theinventions described and/or claimed in this document.

Computing device 600 includes a processor 602, memory 604, a storagedevice 606, a high-speed interface 608 connecting to memory 604 andhigh-speed expansion ports 610, and a low speed interface 612 connectingto low speed bus 614 and storage device 606. Each of the components 602,604, 606, 608, 610, and 612, are interconnected using various busses,and may be mounted on a common motherboard or in other manners asappropriate. The processor 602 can process instructions for executionwithin the computing device 600, including instructions stored in thememory 604 or on the storage device 606 to display graphical informationfor a GUI on an external input/output device, such as display 616coupled to high speed interface 608. In other implementations, multipleprocessors and/or multiple buses may be used, as appropriate, along withmultiple memories and types of memory. Also, multiple computing devices600 may be connected, with each device providing portions of thenecessary operations (e.g., as a server bank, a group of blade servers,or a multi-processor system).

The memory 604 stores information within the computing device 600. Inone implementation, the memory 604 is a computer-readable medium. In oneimplementation, the memory 604 is a volatile memory unit or units. Inanother implementation, the memory 604 is a non-volatile memory unit orunits.

The storage device 606 is capable of providing mass storage for thecomputing device 600. In one implementation, the storage device 606 is acomputer-readable medium. In various different implementations, thestorage device 606 may be a floppy disk device, a hard disk device, anoptical disk device, or a tape device, a flash memory or other similarsolid state memory device, or an array of devices, including devices ina storage area network or other configurations. In one implementation, acomputer program product is tangibly embodied in an information carrier.The computer program product contains instructions that, when executed,perform one or more methods, such as those described above. Theinformation carrier is a computer- or machine-readable medium, such asthe memory 604, the storage device 606, or memory on processor 602.

The high speed controller 608 manages bandwidth-intensive operations forthe computing device 600, while the low speed controller 612 manageslower bandwidth-intensive operations. Such allocation of duties isexemplary only. In one implementation, the high-speed controller 608 iscoupled to memory 604, display 616 (e.g., through a graphics processoror accelerator), and to high-speed expansion ports 610, which may acceptvarious expansion cards (not shown). In the implementation, low-speedcontroller 612 is coupled to storage device 606 and low-speed expansionport 614. The low-speed expansion port, which may include variouscommunication ports (e.g., USB, Bluetooth, Ethernet, wireless Ethernet)may be coupled to one or more input/output devices, such as a keyboard,a pointing device, a scanner, or a networking device such as a switch orrouter, e.g., through a network adapter.

The computing device 600 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as astandard server 620, or multiple times in a group of such servers. Itmay also be implemented as part of a rack server system 624. Inaddition, it may be implemented in a personal computer such as a laptopcomputer 622. Alternatively, components from computing device 600 may becombined with other components in a mobile device (not shown), such asdevice 650. Each of such devices may contain one or more of computingdevice 600, 650, and an entire system may be made up of multiplecomputing devices 600, 650 communicating with each other.

Computing device 650 includes a processor 652, memory 664, aninput/output device such as a display 654, a communication interface666, and a transceiver 668, among other components. The device 650 mayalso be provided with a storage device, such as a microdrive or otherdevice, to provide additional storage. Each of the components 650, 652,664, 654, 666, and 668, are interconnected using various buses, andseveral of the components may be mounted on a common motherboard or inother manners as appropriate.

The processor 652 can process instructions for execution within thecomputing device 650, including instructions stored in the memory 664.The processor may also include separate analog and digital processors.The processor may provide, for example, for coordination of the othercomponents of the device 650, such as control of user interfaces,applications run by device 650, and wireless communication by device650.

Processor 652 may communicate with a user through control interface 658and display interface 656 coupled to a display 654. The display 654 maybe, for example, a TFT LCD display or an OLED display, or otherappropriate display technology. The display interface 656 may compriseappropriate circuitry for driving the display 654 to present graphicaland other information to a user. The control interface 658 may receivecommands from a user and convert them for submission to the processor652. In addition, an external interface 662 may be provide incommunication with processor 652, so as to enable near areacommunication of device 650 with other devices. External interface 662may provide, for example, for wired communication (e.g., via a dockingprocedure) or for wireless communication (e.g., via Bluetooth or othersuch technologies).

The memory 664 stores information within the computing device 650. Inone implementation, the memory 664 is a computer-readable medium. In oneimplementation, the memory 664 is a volatile memory unit or units. Inanother implementation, the memory 664 is a non-volatile memory unit orunits. Expansion memory 674 may also be provided and connected to device650 through expansion interface 672, which may include, for example, aSIMM card interface. Such expansion memory 674 may provide extra storagespace for device 650, or may also store applications or otherinformation for device 650. Specifically, expansion memory 674 mayinclude instructions to carry out or supplement the processes describedabove, and may include secure information also. Thus, for example,expansion memory 674 may be provide as a security module for device 650,and may be programmed with instructions that permit secure use of device650. In addition, secure applications may be provided via the SIMMcards, along with additional information, such as placing identifyinginformation on the SIMM card in a non-hackable manner.

The memory may include for example, flash memory and/or MRAM memory, asdiscussed below. In one implementation, a computer program product istangibly embodied in an information carrier. The computer programproduct contains instructions that, when executed, perform one or moremethods, such as those described above. The information carrier is acomputer- or machine-readable medium, such as the memory 664, expansionmemory 674, or memory on processor 652.

Device 650 may communicate wirelessly through communication interface666, which may include digital signal processing circuitry wherenecessary. Communication interface 666 may provide for communicationsunder various modes or protocols, such as GSM voice calls, SMS, EMS, orMMS messaging, CDMA, TDMA, PDC, WCDMA, CDMA2000, or GPRS, among others.Such communication may occur, for example, through radio-frequencytransceiver 668. In addition, short-range communication may occur, suchas using a Bluetooth, WiFi, or other such transceiver (not shown). Inaddition, GPS receiver module 670 may provide additional wireless datato device 650, which may be used as appropriate by applications runningon device 650.

Device 650 may also communication audibly using audio codec 660, whichmay receive spoken information from a user and convert it to usabledigital information. Audio codex 660 may likewise generate audible soundfor a user, such as through a speaker, e.g., in a handset of device 650.Such sound may include sound from voice telephone calls, may includerecorded sound (e.g., voice messages, music files, etc.) and may alsoinclude sound generated by applications operating on device 650.

The computing device 650 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as acellular telephone 680. It may also be implemented as part of asmartphone 682, personal digital assistant, or other similar mobiledevice.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms “machine-readable medium”“computer-readable medium” refers to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs)) used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term “machine-readable signal” refers to any signal used to providemachine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing system that includes a back end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here), or any combination of such back end, middleware, orfront end components. The components of the system can be interconnectedby any form or medium of digital data communication (e.g., acommunication network). Examples of communication networks include alocal area network (“LAN”), a wide area network (“WAN”), and theInternet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

Although a few implementations have been described in detail above,other modifications are possible. For example, the logic flows depictedin the figures do not require the particular order shown, or sequentialorder, to achieve desirable results. In addition, other steps may beprovided, or steps may be eliminated, from the described flows, andother components may be added to, or removed from, the describedsystems. Accordingly, other implementations are within the scope of thefollowing claims.

What is claimed is:
 1. A method comprising: receiving, at dataprocessing hardware of a video conference system, speech data from afirst computing device, the speech data representing an utterance spokenby a first participant of a video conference and captured by amicrophone of the first computing device; receiving, at the dataprocessing hardware, video data from a second computing device, thevideo data associated with a second participant of the video conferenceand captured by a camera of the second computing device while the firstparticipant was speaking the utterance; transcribing, by the dataprocessing hardware, the speech data representing the utterance spokenby the first participant of the video conference into text in real-time;detecting, by the data processing hardware, an emotional state of thesecond participant based on the video data received from the secondcomputing device; and transmitting, by the data processing hardware, thetext of the speech data to the second computing device based on thedetected emotional state of the second participant, the text of thespeech data when received by the second computing device causing thesecond computing device to display the text of the speech data on avideoconference graphical user interface executing on the secondcomputing device.
 2. The method of claim 1, wherein the video dataassociated with the second participant represents at least one ofgestures, facial expressions, physical characteristics, or body languageof the second participant.
 3. The method of claim 1, wherein the videodata associated with the second participant of the video conferencerepresents facial expressions of the second participant.
 4. The methodof claim 3, wherein detecting the emotional state of the secondparticipant comprises detecting that the second participant isinterested or confused based on the facial expressions of the secondparticipant.
 5. The method of claim 1, further comprising: determining,by the data processing hardware, a topic of the video conference byanalyzing one or more words and/or phrases in the text of the speechdata; annotating, by the data processing hardware, the text of thespeech data by: determining one or more relevant terms in text of thespeech data as being potentially relevant to the determined topic; andidentifying, using the one or more relevant terms in the text, aresource associated with the determined topic of the video conference;generating, by the data processing hardware, a user interface componentfor the identified resource; and outputting, by the data processinghardware, the user interface component for the identified resource tothe second computing device in real-time based on the detected emotionalstate of the second participant, the user interface component whenreceived by the second computing device causing the second computingdevice to display the user interface component on the videoconferencegraphical user interface executing on the second computing device. 6.The method of claim 5, wherein detecting the emotional state of thesecond participant comprises detecting that the second participant isparticularly interested in the determined topic based on the video dataassociated with the second participant and received from the secondcomputing device, the video data associated with the second participantrepresenting at least one of gestures, facial expressions, physicalcharacteristics, or body language of the second participant.
 7. Themethod of claim 5, wherein identifying the resource associated with thedetermined topic of the video conference comprises, obtaining, from anadvertising module, advertising content that corresponds to thedetermined topic of the video conference.
 8. The method of claim 5,wherein identifying the resource associated with the determined topic ofthe video conference comprises: obtaining, from a search engine, one ormore search results that are identified as a result of performing aquery using at least one of the one or more relevant terms in the textof the speech data as being potentially relevant to the determinedtopic; and selecting a particular search result from among the one ormore search results identified as the result of performing the query. 9.The method of claim 5, wherein generating the user interface componentfor the identified resource comprises: identifying an event associatedwith the determined topic of the video conference; and generating aninvitation for the event, the invitation comprising at least one of acalendar date, a time, a location , or a guest list for the identifiedevent.
 10. The method of claim 5, wherein generating the user interfacecomponent for the identified resource comprises: identifying a locationassociated with the determined topic of the video conference; andgenerating a map image associated with the identified location.
 11. Themethod of claim 5, wherein generating the user interface component forthe identified resource comprises: identifying a location associatedwith the determined topic of the video conference; and generating ahyperlink to a map image associated with the identified location.
 12. Avideo conference system comprising: data processing hardware; and memoryhardware in communication with the data processing hardware and storinginstructions, that when executed by the data processing hardware, causethe data processing hardware to perform operations comprising: receivingspeech data from a first computing device, the speech data representingan utterance spoken by a first participant of a video conference andcaptured by a microphone of the first computing device; receiving videodata from a second computing device, the video data associated with asecond participant of the video conference and captured by a camera ofthe second computing device while the first participant was speaking theutterance; transcribing the speech data representing the utterancespoken by the first participant of the video conference into text inreal-time; detecting an emotional state of the second participant basedon the video data received from the second computing device; andtransmitting the text of the speech data to the second computing devicebased on the detected emotional state of the second participant, thetext of the speech data when received by the second computing devicecausing the second computing device to display the text of the speechdata on a videoconference graphical user interface executing on thesecond computing device.
 13. The video conference system of claim 12,wherein the video data associated with the second participant representsat least one of gestures, facial expressions, physical characteristics,or body language of the second participant.
 14. The video conferencesystem of claim 12, wherein the video data associated with the secondparticipant of the video conference represents facial expressions of thesecond participant.
 15. The video conference system of claim 14, whereindetecting the emotional state of the second participant comprisesdetecting that the second participant is interested or confused based onthe facial expressions of the second participant.
 16. The videoconference system of claim 1, wherein the operations further comprise:determining a topic of the video conference by analyzing one or morewords and/or phrases in the text of the speech data; annotating the textof the speech data by: determining one or more relevant terms in text ofthe speech data as being potentially relevant to the determined topic;and identifying, using the one or more relevant terms in the text, aresource associated with the determined topic of the video conference;generating a user interface component for the identified resource; andoutputting the user interface component for the identified resource tothe second computing device in real-time based on the detected emotionalstate of the second participant, the user interface component whenreceived by the second computing device causing the second computingdevice to display the user interface component on the videoconferencegraphical user interface executing on the second computing device. 17.The video conference system of claim 16, wherein detecting the emotionalstate of the second participant comprises detecting that the secondparticipant is particularly interested in the determined topic based onthe video data associated with the second participant and received fromthe second computing device, the video data associated with the secondparticipant representing at least one of gestures, facial expressions,physical characteristics, or body language of the second participant.18. The video conference system of claim 16, wherein identifying theresource associated with the determined topic of the video conferencecomprises, obtaining, from an advertising module, advertising contentthat corresponds to the determined topic of the video conference. 19.The video conference system of claim 16, wherein identifying theresource associated with the determined topic of the video conferencecomprises: obtaining, from a search engine, one or more search resultsthat are identified as a result of performing a query using at least oneof the one or more relevant terms in the text of the speech data asbeing potentially relevant to the determined topic; and selecting aparticular search result from among the one or more search resultsidentified as the result of performing the query.
 20. The videoconference system of claim 16, wherein generating the user interfacecomponent for the identified resource comprises: identifying an eventassociated with the determined topic of the video conference; andgenerating an invitation for the event, the invitation comprising atleast one of a calendar date, a time, a location , or a guest list forthe identified event.
 21. The video conference system of claim 16,wherein generating the user interface component for the identifiedresource comprises: identifying a location associated with thedetermined topic of the video conference; and generating a map imageassociated with the identified location.
 22. The video conference systemof claim 16, wherein generating the user interface component for theidentified resource comprises: identifying a location associated withthe determined topic of the video conference; and generating a hyperlinkto a map image associated with the identified location.